Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Discouraging Words from Machines Impair Human Game Play A new CMU study shows that people who played a game with a humanoid robot known as Pepper performed worse when the robot discouraged them and better when it encouraged them. "This is one of the first studies of human-robot interaction in an environment where they are not cooperating," said co-author Fei Fang, an assistant professor in the Institute for Software Research. Bot Can Beat Humans in Multiplayer Hidden-Role Games MIT researchers have developed a bot, DeepRole, equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret. At the Conference on Neural Information Processing Systems next month, the researchers will present DeepRole. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model In this work, researchers present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era.

Researchers have developed an artificial intelligence-enabled machine that can beat human players in a tricky online multiplayer game where player roles and motives are kept secret, says a study. It was presented at International Conference on Information Systems. The machine, called'DeepRole', is the first gaming bot that can win online multiplayer games in which the participants' team allegiances are initially unclear, according the study from Massachusetts Institute of Technology (MIT), US. The bot is designed with novel "deductive reasoning" added into an AI algorithm commonly used for playing poker. This helps it reason about partially observable actions, to determine the probability that a given player is a teammate or opponent.

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play. Our algorithm integrates deductive reasoning into vector-form CFR to reason about joint beliefs and deduce partially observable actions. We augment deep value networks with constraints that yield interpretable representations of win probabilities. These innovations enable DeepRole to scale to the full Avalon game. Empirical game-theoretic methods show that DeepRole outperforms other hand-crafted and learned agents in five-player Avalon. DeepRole played with and against human players on the web in hybrid human-agent teams. We find that DeepRole outperforms human players as both a cooperator and a competitor.